图形学与多媒体

IEEE Computer Graphics and Applications

Special Issue on Provenance and Logging for Sense Making — Call for Papers

摘要截稿:

全文截稿: 2018-12-01

影响因子: 1.64

期刊难度:

CCF分类: 无

中科院JCR分区:

• 大类 : 工程技术 - 3区

• 小类 : 计算机：软件工程 - 3区

Overview

Sense making is one of the biggest challenges in data analysis faced by both the industry and the research community. It involves understanding the data and uncovering its model, generating a hypothesis, selecting analysis methods, creating novel solutions, designing evaluation, and also critical thinking and learning wherever needed. The research and development for such sense making tasks lags far behind the fast-changing user needs. As a result, sense making is often performed manually and the limited human cognition capability becomes the bottleneck of sense making in data analysis and decision making.

A recent advance in sense making research is the capture, visualization, and analysis of provenance information. Provenance is the history and context of sense making, including the data/analysis used and the users’ critical thinking process. It has been shown that provenance can effectively support many sense making tasks. For instance, provenance can provide an overview of what has been examined and reveal gaps such as unexplored information or solution possibilities. Besides, provenance can support collaborative sense making and communication by sharing the rich context of the sense making process.

Besides data analysis and decision making, provenance has been studied in many other fields, sometimes under different names, for different types of sense making. For example, the Human-Computer Interaction community relies on the analysis of logging to understand user behaviors and intentions; the WWW and database community has been working on data lineage to understand uncertainty and trustworthiness; and finally, reproducible science heavily relies on provenance to improve the reliability and efficiency of scientific research.

For this special issue, we are soliciting papers that describe innovative research, design, system/tools, and viewpoints regarding the collection, analysis, and summarization of provenance information to support the design and evaluation of novel techniques for sense making across different application domains:

Research related to the challenges in capturing the required provenance information, such as:
- The complex provenance information required for different use cases;
- Automatic capture of high-level provenance such as human thinking and reasoning;
- Software architecture for provenance capture for both new and existing systems.

Research related to the analysis and visualization of provenance data, such as:
- Visualization and summarization of provenance information;
- Machine learning and Nature Language Processing techniques that can help analysis of provenance data.